Compulsive hoarding is a common, debilitating, and treatment-resistant syndrome characterized by excessive acquisition, difficulty discarding, and excessive clutter leading to marked impairment and health risk. Although a cognitive-behavioral model of compulsive hoarding is emerging, to date few studies have examined the neurobiological aspects of this syndrome. The aims of the proposed study are: (1) To extend our previous findings suggesting a dysfunction in action monitoring for compulsive hoarding, in which the anterior cingulate cortex (ACC) produces false error signals when making decisions about discarding possessions. This prediction, in keeping with the emerging cognitive-behavioral model of hoarding, is based on the theory that these false error signals are experienced as the feeling that things are "not just right," leading to increased anxiety, inability to resolve the decision-making process, and ultimately a decreased likelihood of discarding. (2) To extend our previous findings suggesting that compulsive hoarding is associated with hyperactivity in frontal-striatal circuits during symptom provocation. The cognitive-behavioral model also suggests that hoarders experience an exaggerated sense of attachment to their possessions. When making decisions about discarding possessions, this attachment increases hoarders'perceived risk of making a wrong decision and thus leads to avoidance of decision-making. Our pilot data suggest that this excessive attachment may be related to activation in orbitofrontal cortex (OFC). (3) To clarify the relationship between ACC action-monitoring dysfunction and phenomenological indices of hoarding such as "not just right experiences" and emotional attachment to objects. In order to validate observed activation patterns, we will relate neural activation to subjective and behavioral indices of decision-making deficits. (4) To examine functional connectivity in compulsive hoarding and control participants during a novel and evocative hoarding-related symptom provocation task. We will use cutting-edge data analytic procedures to examine patterns of interrelated neural activation as well as the time course of activation across the decision making process.